Reduce AI Costs: Token Optimization Techniques

As AI coding tools and agents become standard in developer workflows, token costs are emerging as a significant production challenge. This course equips you with practical techniques for reducing LLM token usage without sacrificing the accuracy or reliability of your AI outputs. Explore strategies including context compression, smart summarization, memory management, and selective context retention—the same approaches used by engineers building efficient, scalable AI systems. Whether you work with tools like Cursor, Copilot, or Claude Code, or build your own agents and RAG pipelines, these techniques apply across the stack. By the end of this course, you’ll be equipped with 10 actionable optimization methods you can apply to reduce costs and improve performance.

Learning objectives
Identify the root causes of excessive token usage in LLM-powered applications and agent workflows.
Apply context compression techniques to reduce token consumption without sacrificing accuracy or output quality.
Implement memory management strategies that help AI agents retain relevant information efficiently across long-running tasks.
Evaluate and optimize your AI workflows for cost efficiency using practical, tool-agnostic techniques.
Diagnose token inefficiencies in tools like Cursor, Copilot, Claude Code, and Codex and apply targeted fixes.